The book is structured to take students from the hardware level up to the algorithmic level:
: The standard classification of computer architectures into categories like SISD, SIMD, and MIMD based on instruction and data streams. Performance Metrics : Deep analysis of scalability , and the overhead of synchronization and communication. Data Dependence Graphs The book is structured to take students from
: Techniques for assessing speedup, efficiency, and scalability of parallel solutions. Chapter Overview Chapter Overview : Students and software engineers with
: Students and software engineers with a background in computer architecture and basic algorithm analysis. The book highlights the importance of parallel computing
"Parallel Computing: Theory and Practice" by Michael J. Quinn provides a comprehensive introduction to the field of parallel computing, covering both theoretical foundations and practical applications. The book highlights the importance of parallel computing in modern computing systems, enabling the efficient processing of complex tasks. As computational demands continue to grow, parallel computing will play an increasingly critical role in addressing the challenges of the 21st century.
A rare gem. Quinn explains NC (Nick’s Class), P-completeness, and why certain problems (like depth-first search) are inherently hard to parallelize. For computer science theory students, this appendix is worth the price of admission alone.
You are a computer science student or a researcher looking to dive into the world of parallel computing. You've heard about the book "Parallel Computing: Theory and Practice" by Michael J. Quinn, which is considered a classic in the field. The book provides a comprehensive introduction to the theory and practice of parallel computing, covering topics such as parallel algorithms, architectures, and programming paradigms.